Data scientist

Job Title: Data Scientist

About the Role
In this role, you will uncover insights and intelligence that enable organizations in dynamic, data‑intensive industries to operate, scale, and innovate. You will build robust, future‑ready machine learning and analytical models that power predictive insights, automation, and data‑driven decision-making across complex digital transformation programmes.

Key Responsibilities 
• Explore, clean, and analyse large, complex datasets to uncover patterns and actionable insights.
• Develop, train, and validate machine learning, statistical, and predictive models for real-world business problems.
• Design and execute experiments (A/B testing, hypothesis testing, simulations) to quantify outcomes and support decision-making.
• Translate business requirements into modelling tasks through close collaboration with engineering, product, and domain teams.
• Build end‑to‑end ML pipelines including feature engineering, preprocessing, and deployment-ready outputs.
• Apply advanced techniques such as NLP, forecasting, anomaly detection, optimisation, or LLM/GenAI where relevant.
• Implement model evaluation using offline metrics, cross‑validation, online experiments, and human-in-the-loop feedback loops.
• Communicate insights clearly through dashboards, visualisations, and stakeholder-centric presentations.

Essential Skills & Experience
• Hands-on experience with GenAI, Gemini, or open-source LLMs; ability to build applications for code translation, text extraction, summarisation, and SDLC optimisation.
• Strong experience with AI Agents, Chatbots, RAG pipelines, and vector databases (PGVector, ChromaDB).
• Proficiency with GenAI evaluation tools such as Pegasus, Ragas, or DeepEval.
• Ability to build conversational interfaces using ReactJS or similar front-end frameworks.
• Experience developing and deploying AI agents using LangGraph, ADK, A2A, or MCP.
• Strong programming skills in Python (LangChain, LangGraph, LangSmith) and TypeScript (preferred).
• Solid understanding of LLMs, prompt engineering, and graph-based workflow orchestration.
• Knowledge of input/output guardrails: hallucination reduction, PII filtering, HAP, bias mitigation.
• Experience implementing security best practices including spike arrest strategies, DDoS mitigation, and denial-of-wallet protections.
• Knowledge of API gateways, ISTIO, and ability to diagnose failures in end‑to‑end communication.
• Hands-on experience with API development and microservices architecture.

Desirable Skills & Experience
• Strong experience applying ML, statistical modelling, and predictive analytics to business problems.
• Ability to resolve end‑to‑end connectivity and data integration challenges with cross‑functional teams.
• Experience with data cleansing, feature engineering, and exploratory data analysis for large datasets.
• Familiarity with NLP, embeddings, prompt engineering, or LLM fine‑tuning.
• Experience building end‑to‑end ML pipelines including validation, optimisation, deployment, and monitoring.
• Understanding of MLOps practices such as model registries, CI/CD for ML, and automated training/inference workflows.
• Ability to translate business problems into analytical solutions with clear communication to technical and non‑technical audiences.
• Knowledge of data governance, data quality, lineage, ethics, privacy, and responsible AI principles.
• Experience with cloud platforms (GCP preferred) for scalable training and deployment.
• A growth mindset with interest in new algorithms, tools, and emerging AI/ML innovations.